As shown in fig:soundimage, the ultrasound images do not have the shape of a person, and their edges are very different from those of the segmentation images.
When there is a lot of ambiguity in the input image or when the difference between input and output is large, it is better to use the probabilistic method than the deterministic method.
Thus, we consider performing human segmentation based on probabilistic models.
Kohl et al. [1]} proposed a probabilistic U-Net combining VAE [2]} with U-Net.
This method learns prior and posterior networks, which output the latent spaces of input and input/segmentation images, respectively, to be close.
The latent variables sampled from the latent space of the posterior network are added to the last layer of U-Net.
This method deals with large discrepancies between the input and output images by sampling latent variables from the latent space output by the prior network during the inference phase.
However, it is difficult to handle with the existing prior network because the difference in appearance between the ultrasound and segmentation images is huge.
Thus, we propose collaborative learning probabilistic U-net based on probabilistic U-Net.
Details of the proposed method are described below.
<FIGURE>